{"title":"A Global Extended MODIS-Compatible NDVI Dataset","authors":"Tingting Zhang;Hongyan Zhang;Yeqiao Wang;Tao Xiong;Meiyu Wang;Zhengxiang Zhang;Xiaoyi Guo;Jianjun Zhao","doi":"10.1109/JSTARS.2025.3550416","DOIUrl":null,"url":null,"abstract":"The study of climate change impacts on vegetation requires access to long-time series vegetation dynamics. MODIS NDVI, with its high chlorophyll sensitivity and data quality, is an important data source in global change monitoring and ecological studies. However, as MODIS NDVI became available only after 2000, the data before 2000 are lacking. This article provides a global MODIS-compatible NDVI dataset at moderate spatial (0.05°) and temporal (16-day) resolution from 1982 to 2000. This dataset generates a long-time series of global NDVI products based on MODIS and AVHRR data using multiple optimization machine learning algorithms. It is designed to synchronously capture complex spatial and temporal correlations of multisource data and account for heterogeneity. Compared with MODIS NDVI, R<sup>2</sup> of this dataset ranged from 0.79 to 0.95, and the mean absolute error was less than 0.06 in most areas. This dataset addresses the problem of the short period of MODIS NDVI data and provides a new data choice for monitoring global vegetation dynamics and ecological studies.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"8390-8398"},"PeriodicalIF":4.7000,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10921702","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10921702/","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
The study of climate change impacts on vegetation requires access to long-time series vegetation dynamics. MODIS NDVI, with its high chlorophyll sensitivity and data quality, is an important data source in global change monitoring and ecological studies. However, as MODIS NDVI became available only after 2000, the data before 2000 are lacking. This article provides a global MODIS-compatible NDVI dataset at moderate spatial (0.05°) and temporal (16-day) resolution from 1982 to 2000. This dataset generates a long-time series of global NDVI products based on MODIS and AVHRR data using multiple optimization machine learning algorithms. It is designed to synchronously capture complex spatial and temporal correlations of multisource data and account for heterogeneity. Compared with MODIS NDVI, R2 of this dataset ranged from 0.79 to 0.95, and the mean absolute error was less than 0.06 in most areas. This dataset addresses the problem of the short period of MODIS NDVI data and provides a new data choice for monitoring global vegetation dynamics and ecological studies.
期刊介绍:
The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.